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<!-- ==================== MODULE DESCRIPTION ==================== -->
<h1 class="epydoc">Module lhs</h1><p class="nomargin-top"><span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html">source&nbsp;code</a></span></p>
<!-- ==================== FUNCTIONS ==================== -->
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          <td><span class="summary-sig"><a href="BIP.Bayes.lhs-module.html#lhsFromSample" class="summary-sig-name">lhsFromSample</a>(<span class="summary-sig-arg">sample</span>,
        <span class="summary-sig-arg">siz</span>=<span class="summary-sig-default">100</span>)</span><br />
      Latin Hypercube Sample from a set of values.</td>
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            <span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhsFromSample">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="BIP.Bayes.lhs-module.html#lhsFromDensity" class="summary-sig-name">lhsFromDensity</a>(<span class="summary-sig-arg">kde</span>,
        <span class="summary-sig-arg">siz</span>=<span class="summary-sig-default">100</span>)</span><br />
      LHS sampling from a variable's Kernel density estimate.</td>
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            <span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhsFromDensity">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="BIP.Bayes.lhs-module.html#lhs" class="summary-sig-name">lhs</a>(<span class="summary-sig-arg">dist</span>,
        <span class="summary-sig-arg">parms</span>,
        <span class="summary-sig-arg">siz</span>=<span class="summary-sig-default">100</span>,
        <span class="summary-sig-arg">noCorrRestr</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">corrmat</span>=<span class="summary-sig-default">None</span>)</span><br />
      Latin Hypercube sampling of any distribution.</td>
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            <span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhs">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="BIP.Bayes.lhs-module.html#rank_restr" class="summary-sig-name">rank_restr</a>(<span class="summary-sig-arg">nvars</span>=<span class="summary-sig-default">4</span>,
        <span class="summary-sig-arg">smp</span>=<span class="summary-sig-default">100</span>,
        <span class="summary-sig-arg">noCorrRestr</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">Corrmat</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns the indices for sampling variables with
the desired correlation structure.</td>
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            <span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#rank_restr">source&nbsp;code</a></span>
            
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<a name="lhsFromSample"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">lhsFromSample</span>(<span class="sig-arg">sample</span>,
        <span class="sig-arg">siz</span>=<span class="sig-default">100</span>)</span>
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  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhsFromSample">source&nbsp;code</a></span>&nbsp;
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  Latin Hypercube Sample from a set of values.
For univariate distributions only
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>sample</code></strong> - list, tuple of array</li>
        <li><strong class="pname"><code>siz</code></strong> - Number or shape tuple for the output sample</li>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">lhsFromDensity</span>(<span class="sig-arg">kde</span>,
        <span class="sig-arg">siz</span>=<span class="sig-default">100</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhsFromDensity">source&nbsp;code</a></span>&nbsp;
    </td>
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  LHS sampling from a variable's Kernel density estimate.
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>kde</code></strong> - scipy.stats.kde.gaussian_kde object</li>
        <li><strong class="pname"><code>siz</code></strong> - Number or shape tuple for the output sample</li>
    </ul></dd>
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<a name="lhs"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">lhs</span>(<span class="sig-arg">dist</span>,
        <span class="sig-arg">parms</span>,
        <span class="sig-arg">siz</span>=<span class="sig-default">100</span>,
        <span class="sig-arg">noCorrRestr</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">corrmat</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#lhs">source&nbsp;code</a></span>&nbsp;
    </td>
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  Latin Hypercube sampling of any distribution.
dist is is a scipy.stats random number generator
such as stats.norm, stats.beta, etc
parms is a tuple with the parameters needed for
the specified distribution.
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>dist</code></strong> - random number generator from scipy.stats module or a list of them.</li>
        <li><strong class="pname"><code>parms</code></strong> - tuple of parameters as required for dist, or a list of them.</li>
        <li><strong class="pname"><code>siz</code></strong> - number or shape tuple for the output sample</li>
    </ul></dd>
  </dl>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">rank_restr</span>(<span class="sig-arg">nvars</span>=<span class="sig-default">4</span>,
        <span class="sig-arg">smp</span>=<span class="sig-default">100</span>,
        <span class="sig-arg">noCorrRestr</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">Corrmat</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.Bayes.lhs-pysrc.html#rank_restr">source&nbsp;code</a></span>&nbsp;
    </td>
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  Returns the indices for sampling variables with
the desired correlation structure.
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>nvars</code></strong> - number of variables</li>
        <li><strong class="pname"><code>smp</code></strong> - number of samples</li>
        <li><strong class="pname"><code>noCorrRestr</code></strong> - No correlation restriction if True</li>
        <li><strong class="pname"><code>Corrmat</code></strong> - Correlation matrix. If None, assure uncorrelated samples.</li>
    </ul></dd>
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